User Modeling and User-Adapted Interaction

, Volume 18, Issue 1, pp 81–123

Modeling self-efficacy in intelligent tutoring systems: An inductive approach

  • Scott W. McQuiggan
  • Bradford W. Mott
  • James C. Lester
Original Paper

DOI: 10.1007/s11257-007-9040-y

Cite this article as:
McQuiggan, S.W., Mott, B.W. & Lester, J.C. User Model User-Adap Inter (2008) 18: 81. doi:10.1007/s11257-007-9040-y

Abstract

Self-efficacy is an individual’s belief about her ability to perform well in a given situation. Because self-efficacious students are effective learners, endowing intelligent tutoring systems with the ability to diagnose self-efficacy could lead to improved pedagogy. Self-efficacy is influenced by (and influences) affective state. Thus, physiological data might be used to predict a student’s level of self-efficacy. This article investigates an inductive approach to automatically constructing models of self-efficacy that can be used at runtime to inform pedagogical decisions. It reports on two complementary empirical studies. In the first study, two families of self-efficacy models were induced: a static self-efficacy model, learned solely from pre-test (non-intrusively collected) data, and a dynamic self-efficacy model, learned from both pre-test data as well as runtime physiological data collected with a biofeedback apparatus. In the second empirical study, a similar experimental design was applied to an interactive narrative-centered learning environment. Self-efficacy models were induced from combinations of static and dynamic information, including pre-test data, physiological data, and observations of student behavior in the learning environment. The highest performing induced naïve Bayes models correctly classified 85.2% of instances in the first empirical study and 82.1% of instances in the second empirical study. The highest performing decision tree models correctly classified 86.9% of instances in the first study and 87.3% of instances in the second study.

Keywords

Affective user modelingAffective student modelingSelf-efficacyIntelligent tutoring systemsInductive learningHuman-computer interaction

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  • Scott W. McQuiggan
    • 1
  • Bradford W. Mott
    • 2
  • James C. Lester
    • 1
  1. 1.Department of Computer ScienceNorth Carolina State UniversityRaleighUSA
  2. 2.Emergent Game TechnologiesChapel HillUSA